I. Field of the Invention
The present invention relates generally to image anomaly detection in a target area and, more particularly, to a methodology for analyzing polarimetric sensor data of the target area to detect image anomalies. The methodology can be used for classifying and/or discriminating between object classes in the target area, such as: manmade objects and natural objects, for example.
II. Description of Related Art
Detection of anomalies, using data produced by remote sensing technologies, represents a critical technology for many applications, including intelligence, surveillance, and reconnaissance. Some conventional methods have utilized linearly polarized infrared or thermal radiances of the target area.
Most, if not all, of conventional infrared anomaly detection systems are based on the premise that manmade objects, such as tanks, trucks, and any other equipment, tend to retain thermal energy (hence, to increase their temperatures) from continuous and direct exposure to the environment, and/or a major source of thermal energy (e.g., the sun), more rapidly than natural objects do, such as grass, ground, trees, foliage, and the like. The result from this rapid heat retention by manmade objects is that these objects also dissipate thermal energy more rapidly than natural objects do.
Over a daily period, manmade objects within the target area typically absorb, and thus emit, infrared (thermal) energy more rapidly than natural objects do, beginning at sunrise and continuing throughout the day until a few hours after sunset. This trend difference between the two object classes continues throughout the night until the next sunrise, when the diurnal cycle is repeated. Consequently, the emission of infrared energy (or radiation) by manmade objects in the target area, and potential thermal feature separation of these manmade objects from natural objects, is highly dependent upon the time of day. In addition, manmade surfaces are also known for emitting polarized infrared radiance—independently of their emission rate, as these surfaces tend to be smooth relative to the length of infrared electromagnetic waves in corresponding regions of the spectrum. This is in contrast to surfaces of natural objects, where their emitted radiance generally yields no favoritism to any particular polarization, as these natural surfaces are rough (not smooth) relative to the same infrared electromagnetic wavelengths.
Existing infrared polarimetric anomaly detection systems may utilize polarimetric imagery from passive sensors in which four infrared polarization filtered pixel images of the target area are obtained by these sensors. These systems output and process imagery in the form, known in the remote sensing community, as the Stokes polarization parameters (S0,S1,S2), or Stokes vector, using the four infrared polarization filtered pixel images (I0°,I45°,I90°,I135°) as input, where the subscripts of I label the corresponding polarization component angles; these relationships are expressed as
            [                                                  S              0                                                                          S              1                                                                          S              2                                          ]        =          [                                                                  (                                                      I                                          0                      ⁢                      °                                                        +                                      I                                          45                      ⁢                      °                                                        +                                      I                                          90                      ⁢                      °                                                        +                                      I                                          135                      ⁢                      °                                                                      )                            /              2                                                                                          I                                  0                  ⁢                  °                                            -                              I                                  90                  ⁢                  °                                                                                                                        I                                  45                  ⁢                  °                                            -                              I                                  135                  ⁢                  °                                                                        ]        ,where another popular metric, known in the same community as the degree of linear polarization (DOLP), is readily determined:
  DOLP  =                                          S            2            1                    +                      S            2            2                                      S        0              .  
Of the three Stokes parameters, S1 is usually considered the most effective in detecting manmade objects in the target area, which is believed to be overwhelmingly composed by natural objects. However, when the probability distribution function of the Stokes S1 parameter is plotted, the S1 parameter for manmade objects often heavily overlaps the probability distribution function for natural objects, such that separation in the S1 domain of manmade objects from natural objects in the target area is difficult, if not impossible, to achieve. The overlapping of the probability distribution functions of the Stokes S1 parameter for manmade objects and natural objects also varies as a function of the time of day. Thus, anomaly detection at certain time periods, e.g. early morning, not only is very difficult, but it is also highly prone to error (e.g., false positives).
Still other methods have been attempted to detect and separate manmade objects in a target area from natural objects also in the target area. These other methods have included analysis of the DOLP metric, the Fresnel ratio, as well as multispectral polarimetric measurements. None of these methods, however, have significantly improved the detection of manmade objects in the target area as compared with the analysis of the Stokes S1 parameter, using data produced by either a passive or active sensor from a short range between sensor and objects in a controlled laboratory environment. Moreover, the use of Stokes S1 parameter has also been shown to be significantly less effective when data are collected in an uncontrolled outdoor environment, especially when a passive sensor is used for data acquisition at a range greater than 200 m. In practice, commercial and military remote sensing surveillance applications require ranges to be greater than 300 m, and users executing surveillance operations favor the application of passive rather than active sensors for reasons to be explained later. Local surface orientation, constructive and destructive measurement interferences are often cited as sources of performance degradation using passive Stokes S1 measurements from an uncontrolled outdoor environment.